Espen Hagen1, Torbjørn V Ness2, Amir Khosrowshahi3, Christina Sørensen4, Marianne Fyhn4, Torkel Hafting4, Felix Franke5, Gaute T Einevoll6. 1. Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Aas, Norway; Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and JARA, 52425 Jülich, Germany. Electronic address: e.hagen@fz-juelich.de. 2. Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Aas, Norway. 3. Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Aas, Norway; Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA 94720-3198, USA; Nervana Systems, San Diego, CA 92121, USA. 4. Hafting-Fyhn Neuroplasticity Group, Department of Biosciences, University of Oslo, P.O. Box 1066 Blindern, NO-0316 Oslo, Norway. 5. Bio Engineering Laboratory, Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology Zürich, CH-4058 Basel, Switzerland. 6. Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Aas, Norway; Department of Physics, University of Oslo, P.O. Box 1066 Blindern, NO-0316 Oslo, Norway.
Abstract
BACKGROUND: New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. NEW METHOD: We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy. RESULTS: ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. COMPARISON WITH EXISTING METHODS: ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. CONCLUSION: ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity.
BACKGROUND: New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. NEW METHOD: We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy. RESULTS: ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. COMPARISON WITH EXISTING METHODS: ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. CONCLUSION: ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity.
Authors: Espen Hagen; David Dahmen; Maria L Stavrinou; Henrik Lindén; Tom Tetzlaff; Sacha J van Albada; Sonja Grün; Markus Diesmann; Gaute T Einevoll Journal: Cereb Cortex Date: 2016-10-20 Impact factor: 5.357
Authors: Réka Barbara Bod; János Rokai; Domokos Meszéna; Richárd Fiáth; István Ulbert; Gergely Márton Journal: Front Neuroinform Date: 2022-06-13 Impact factor: 3.739
Authors: Helena T Głąbska; Eivind Norheim; Anna Devor; Anders M Dale; Gaute T Einevoll; Daniel K Wójcik Journal: Front Neuroinform Date: 2016-01-25 Impact factor: 4.081